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The paper critically examines CLadder, a popular benchmark for evaluating causal reasoning in LLMs, revealing that it embeds superficial, highly predictive patterns. Simple models like random forests perform well on its generated datasets, and commercial LLMs' performance is brittle, suggesting their strong results come from exploiting shortcuts rather than genuine causal reasoning.